In many situations such as automatic maneuvering of airborne objects, underwater objects, objects in the outer space, and for surveillance of objects it is necessary to identify objects in a targeted region and to determine their locations and/or motions. To this end, sensors or imaging devices such as high-resolution cameras, radars, lidars, etc. are used, and the raw sensor signals are processed to extract actionable knowledge, such as identification of an object of interest, such as a vehicle in a field, a satellite launched in an orbit, etc. Actionable knowledge or information of interest can also include determining whether and how an object may be moving, such as the speed and direction of a vehicle, whether an object in space is tumbling or has left an orbit, etc.
Often, significant amounts of data (e.g., tens or hundreds of megabytes, several gigabytes, or more) must be collected from the sensor and a large number of computations (e.g., hundreds, thousands, millions, billions, or more floating-point operations) must be performed on the collected data in order to extract useful, actionable information. As such, many sensor and associated data processing systems are bulky and costly, and they generally consume a lot of power. Compressive sensing can address some of these challenges. In particular, taking advantage of the fact that the collected images are often sparse, i.e., only a fraction of the image data is associated with the object(s) of interest and a large portion (e.g., more than 50%) of the image is associated with background only, the sensors are adapted to collect only a limited number of samples and not the total number of available samples. The collected data is generally called compressively sensed data or compressed data. A co-pending U.S. patent application Ser. No. 14/699,871, entitled “Systems and Methods for Joint Angle-Frequency Determination” describes using compressed data for joint determination of angles and frequencies of signals received from targets emitting such signals.
The size, cost, and/or power consumption of the sensor, and the amount of data to be transmitted to a data processor from the sensor can thus be reduced substantially, e.g., by 20%, 30%, 40%, 50%, 60%, etc. Typically, the compressed data is reconstructed by the data-processor to obtain a reconstructed image and the reconstructed image may then be processed to extract actionable knowledge or information of interest. The reconstruction process can increase the data processing burden substantially and, as such, the size, cost, and/or power consumption of the data processing system can increase, and the performance thereof can decrease.